Combined formаtion of a cryptographic key using synchronized artificial neural networks

А combined method for forming a cryptographic key is proposed in the article. The proposed combined formation consists of two stages: the formation of partially coinciding binary sequences using synchronized artificial neural networks and the elimination of mismatched bits by open comparison of the parities of bit pairs. In this paper, possible vulnerabilities of the basic method of forming a cryptographic key using synchronized artificial neural networks are considered, their danger is assessed, and a correction of the method is proposed to ensure the required confidentiality of the generated shared secret. At the first stage, a deferred brute-force attack is considered. To neutralize this attack, it is proposed to use the convolution function of the results of several independent synchronizations. As a convolution function, the bitwise addition modulo 2 of the vectors of the weights of the networks is used. Due to the correction of the first stage of the basic algorithm, the amount of deferred search exponentially increases, and frequency analysis of binary sequences also becomes ineffective. At the second stage, an attack based on the knowledge of pair parities is considered, taking into account the proposed method for correcting the first stage. The analysis of the influence of network parameters on the process of eliminating the bit mismatch at the second stage is carried out. Statistical modeling of this analysis has been performed. The results obtained showed that the cryptanalyst could not uniquely distinguish the values of the remaining bits. The proposed combined method makes it possible to increase the confidentiality of the generated shared secret and significantly reduce the number of information exchanges in comparison with the Neural key generation technology.

Deep Learning Framework to Detect the False Analysis of a Product given by Robots and Malicious Users

Product evaluations are precious for upcoming clients in supporting them make choices. To this, numerous mining techniques have been proposed, wherein judging a evaluation sentence’s orientation (e.g. Outstanding or bad) is considered as one of their key worrying conditions. Lately, deep studying has emerged as a powerful technique for fixing sentiment kind issues. A neural network intrinsically learns useful instance routinely without human efforts. But, the fulfilment of deep getting to know pretty is primarily based totally on the supply of big-scale education data. We recommend a unique deep studying framework for product review sentiment classification which employs prevalently to be had rankings as susceptible supervision signs and symptoms. The framework consists of steps: (1) studying a high level representation (an embedding region) which captures the general sentiment distribution of sentences thru score facts; (2) such as a class layer-on top of the embedding layer and use labelled sentences for supervised fine-tuning. We discover styles of low stage community structure for modelling evaluation sentences, specifically, convolution function extractors and prolonged brieftime period memory. To have a take a look at the proposed framework, we gather a data set containing 1.1M weakly classified evaluate sentences and eleven, 754 labelled review sentences from Amazon. Experimental effects display the efficacy of the proposed framework and its superiority over baselines. In this future work todetect false reviews given by robots or by malicious people by taking amount, sometimessome companies may hire people to boost their product ranking higher by assigning fake rating and this malicious people or robots give continuous ranking or review to such product and we can detect such fake rating by analysingratingandremove suchfake rating to give only genuine reviews to users.

SDE based Generalized Innovation Diffusion Modeling

Diffusion models are rigorously implemented in marketing research to predict the actual trend of innovations over time. These models can be classified in terms of deterministic and stochastic behavior. Deterministic models ignore the randomness in the adoption rate of an innovation that occurs due to environmental and internal system disturbances. Therefore, in the present research, a generalized stochastic diffusion model using Itô’s process is proposed that jointly study the product awareness and eventual adoption of an innovation. Convolution function is applied to integrate these two processes. In addition, different probability distributions are employed, which are relevant for describing the product awareness and adoption processes. Non-linear regression is further carried out to validate the proposed models and parameters are estimated based on the actual sales data from Smartphone and automobile industries. The forecasting results indicate that the proposed models perform empirically better than the already established diffusion models.

A Class of Abstract Linear Representations for Convolution Function Algebras over Homogeneous Spaces of Compact Groups

This paper introduces a class of abstract linear representations on Banach convolution function algebras over homogeneous spaces of compact groups. LetGbe a compact group andHa closed subgroup ofG. Letμbe the normalizedG-invariant measure over the compact homogeneous spaceG/Hassociated with Weil's formula and. We then present a structured class of abstract linear representations of the Banach convolution function algebrasLp(G/H,μ).

The Improvement of the Region Homogeneity Measure Operator and Its Noise Disposed

Based on the analysis of the characteristic of the image texture, we extend the region homogeneity measure operator (RHMO) to a more reasonable case, thus avoiding some omission situation. Moreover, we compare the RHMO with the convolution function to find out the more rational range of region homogeneity measure (RHM). At the end of this paper, we use the improved RHM method for some images in a new Partial Differential Equation (PDE); the results indicate that the improved concept has achieved a good result.

Convolutions and mean square estimates of certain number-theoretic error terms

We study the convolution function C[f(x)]:=\int_1^x f(y)f\Bigl(\frac xy\Bigr)\frac{dy}y When f(x) is a suitable number-theoretic error term. Asymptotics and upper bounds for C[f(x)] are derived from mean square bounds for f(x). Some applications are given, in particular to |\zeta(\tfrac12+ix)|^{2k} and the classical Rankin--Selberg problem from analytic number theory.